Oct 2026· Discover Civil Engineering· 63 references
Concrete and Cement Materials Research
Abstract
Abstract Foamed concrete is lightweight and insulating, but its mix design is a headache. Strength, durability, and carbon footprint all fight each other. This review looks at how researchers have tried to optimize these trade-offs using methods like RSM, Taguchi, ANN, and genetic algorithms. RSM is easy to interpret but struggles with nonlinear foam behavior. AI models handle nonlinearity better but need lots of data and tell you nothing about why they work. The best path forward seems to be hybrid approaches. On the environmental side, cement drives most of the carbon. Replacing it with SCMs can cut GWP by 20–50%. We put together a conceptual Pareto front showing the sweet spot: 900–1200 kg/m³ density, 8–15 MPa strength, and 350–500 kg CO₂-eq/m³. Big gaps remain. No standard testing protocols. Pore-scale physics is missing from models. Almost nobody has coupled LCA with real optimization. Field validation is rare. We end with a research roadmap: hybrid frameworks, open databases, and proper microstructural inputs.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
